Bayisa Taye.pptx

Contention-Based SCMA for NB-IoT Uplink Communication using
Finite Memory Sequential Learning
Bayisa Taye MULATU
Advisor: Prof. Shigeru Shimamoto
August 1,2018
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Email : bd@archermind.com Web : www.archermind.com Project : General Usage Version: 1.0 Author: ArcherMind Marketing Confidential
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OUTLINE:
 BACKGROUND: NARROW-BAND IOT
 SPARSE-CODE MULTIPLE ACCESS
 NB-PULSE SHAPING
 CONTENTION-BASED SCMA RESOURCE DEFINITION
 CONTENTION-BASED SCMA VERSUS OFDMA
 CB-SCMA USING FMSL
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES

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BACKGROUND: NARROW-BAND IOT
 NB-IoT provides deep-indoor coverage with low-cost and low-
power devices
 3 modes of operation by 3GPP, support massive connectivity of
low-power wide area network(LPWAN)
GSM NB-IOT(Standalone-200kHz)
NB-IoT(In-band-180kHz) NB-IOT(guard-band)
Fig. 1. Modes of operation in NB-IoT

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Contention-based(CB) SCMA is applied in NB-IoT in order to:
 Remove or reduce resource allocation and control
signaling overhead.
 Reduce transmission latency
 Support massive connectivity
MOTIVATION

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SPARSE-CODE MULTIPLE ACCESS (SCMA)
Fig 2: SCMA system

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DESCRIPTION OF SCMA SYSTEM
 Codebooks 𝑱 = 𝑪𝑲
𝑵
; N=non-zero entries, K = codeword
length
 𝑩 = [𝒃𝟏 𝒃𝟐 …
𝒃𝟔]--> information bits from 6 users
 𝑿𝒌
=[𝒙𝟏
𝒌
, 𝒙𝟐
𝒌
, … , 𝒙𝟒
𝒌
]𝑻
--> transmitted symbols at 𝒌𝒕𝒉
user
 The received signal at the eNB:
𝒚 = 𝒌=𝟏
𝟒
‫ܪ‬݇‫ݔ‬݇ +Z ;
 𝑯𝒌 and Z -->channel gain and AWGN vectors
 MPA(Log-MPA) based multi-user detection is used to detect the
symbols from y.

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NB-PULSE SHAPING
 OFDM systems use IFFT to map modulated symbols to multiple
sub-carriers
 IFFT is not suitable for low cost NB-IoT devices
 Frequency localized NB-pulse shaping is done by digital up-
sampler and digital-up converter
 This will simplify the signal processing complexity of NB-SCMA
system
Channel
coding
SCMA
encoder
NB-Pulse
shaping
Fading
channel
UE
Codebook
Fig 3: NB-SCMA system

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CB-SCMA RESOURCE DEFINITION
f
t
Contention Region
Fig. 4. CB-SCMA resource definition
 Contention Transmission Unit (CTU) = f( f, t, Cj , Pl)
 J unique codebooks in a contention region
 L unique pilot sequences for each codebook
LXJ CTUs

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CAPACITY GAIN OF CB-SCMA
 N out of K resources occupied in SCMA; all K occupied in OFDMA
 Capacity gain of SCMA over OFDMA=>In terms of number of
connections
 Gain =
𝐶𝐾
𝑁
xLx𝒎1
Kx𝒎2
𝑵𝑺𝑪𝑴𝑨 = 𝑪𝑲
𝑵
xLx𝒎𝟏
𝑵𝑶𝑭𝑫𝑴𝑨 = 𝐊x𝒎𝟐
𝑵 ≥ 𝟐, 𝟐 ≤ 𝑵 ≤K, 𝑳 ≥ 𝟐
𝒎1 = 𝒎2 = 𝑚 is assumed
(average number of UEs
in a CTU)
K=8,N=4 -> gain of 35
Fig 5: Capacity gain of CB-SCMA over OFDMA

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PACKET DROP RATE OF CB-SCMA
 A packet drop rate is: 𝒓𝒍𝒐𝒔𝒕=𝟏−(𝟏−𝞪)𝒎−𝟏
 𝝰 is probability that each user sends packet at anytime
Fig 6: Packet Drop Rate of CB-SCMA
The effect of packet
collision is higher when
more users share same CTU
Finite memory sequential
learning (FMSL) is
proposed for the above
systems

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CB-SCMA USING FMSL
Fig 7: CB-SCMA using finite memory sequential learning
Central Base
Station
IoT devices
communicate private
signals
M2M
IoT device
CB-SCMA

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FINITE MEMORY SEQUENTIAL LEARNING
NB-IoT devices use FMSL to learn about the underlying
binary states ϴ (0 or 1) about a critical event and
exchange private signals sequentially
 The learning framework uses the following information:
𝑒𝑗−𝐹+4 , … , 𝑒𝑗−1, 𝑇𝑗−1, 𝑄𝑗−1, 𝑖, 𝐶𝑖
 𝑒𝑗−𝑭+4 , … , 𝑒𝑗−1 ->Previous private signals
 𝑇𝑗−1 ->Current estimate of true ϴ
 𝑄𝑗−1, ->update information for true ϴ
 𝑖, 𝐶𝑖 ->indicates critical messages and CTUs used
A NB-IoT device will determine its private signal(belief) 𝑥𝑖 by
maximum likelihood estimation:
𝑥𝑖 = 𝑎𝑟𝑔max
ϴ
𝑃𝑟(𝑒𝑗−𝑭+4 , … , 𝑒𝑗−1/ϴ)

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PERFORMANCE OF FMSL
 Performance of FMSL depends on geographical location: Area A
 Distance from critical event: 𝑟𝑑
 Communication range among the devices: 𝑟𝑐
 Total number of devices that correctly learn true ϴ:
𝑼𝜋𝑟𝑑
2
4𝐴
≤ 𝐸 𝐿 ≤
𝑼𝜋𝑟𝑑
2
𝐴
 Effective Observation radius
𝑟𝑑
′
= 𝑟𝑑 +(𝐹 − 4) 𝑟𝑐
 𝐸 𝐿𝑡 :for occurrence after time t, 𝑟𝑡 =min(𝑟𝑐t, 𝑟𝑑
′
)
𝑼𝜋𝑟𝑡
2
4𝐴
≤ 𝐸 𝐿𝑡 ≤
𝑼𝜋𝑟𝑡
2
𝐴

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PROBABILITY OF SUCCESS AND COLLISION
 Probability of successful transmission:
𝑈𝜋𝑟𝑡
2
4𝐴. 𝐶. 𝑇𝑚𝑖𝑛
≤ 𝐸 𝑃𝑡 ≤
𝑈𝜋𝑟𝑡
2
𝐴. 𝐶. 𝑇𝑚𝑖𝑛
 We assume the binomial distribution to analyze collision probability
 The Collision probability of a conventional UE-CTU mapping:
𝑃𝑐𝑜𝑛𝑣 =
𝐶 𝑘=2
𝑚
𝑝𝑚.𝑘.𝑝𝑘 𝑘=1
𝑚−1
𝑝𝒎−1.𝒌.𝑝 𝑘+1 +𝐶(1− 𝑘=2
𝑚
𝑝𝒎.𝒌.𝑝 𝒌=2
𝒎
𝑝𝒎.𝒌.𝑝𝑘)
𝐶𝑚𝑝
 The Collision Probability for SL based CTU allocation:
𝑃𝑆𝐿 =
( 𝑘 + 1 1 − 𝑚𝑎 − 𝑚 1 − 𝑎 𝑚𝑝 1 − 𝑞𝑚−1 +
𝑚 1 − 𝑎 − 𝑘(1 − 𝑚𝑎)( 𝒎 + 1)𝑝 1 − 𝑞𝒎−1 )
𝑚𝑝
𝑎 = 𝒌=2
𝒎
𝑝𝒎.𝒌.𝑝 = 𝒌=2
𝒎 𝑚
𝑝 𝑝𝑘𝑞𝒎−𝑘

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PARAMETERS USED
 U ⇒ Number of devices deployed in the area A
 𝐂 ⇒The number of CTUs
 𝐓𝐦𝐢𝐧 ⇒Minimum periodicity of periodic messages
 m⇒Average number of devices in a CTU,
 𝑝 = 1 − 𝑞 ⇒traffic rate
 k ⇒ number of devices transmitting at the same time
within a CTU

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RESULTS AND DISCUSSION(1)
Fig. 8. Probability of successful transmission
 probability of successful transmission increases with F

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Fig. 9. Number of devices that learn true ϴ(F=14, U=1500, C=280)
 number of devices that learn correctly increases with time
and F

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Fig. 10. Comparison of probability of collision
 Lower average collision rate than the conventional
system

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CONCLUSIONS
The contention based SCMA with FMSL system shows that
performance in terms probability of successful transmission
and the number of IoT devices that will correctly learn about
the status of the nearby devices increases with memory size F.
 Hence, FMSL improves system performance in terms of
delivery of critical messages which are assigned certain codes
after the learning process converges.
When the devices are assigned certain codes after the learning
process converges, the probability of collision decreases as
compared to conventional systems.

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REFERENCES
[1]. Y. D. Beyene, R. Jantti, K. Ruttik and S. Iraji, "On the Performance of Narrow-
Band Internet of Things (NB-IoT)," 2017 IEEE Wireless Communications and
Networking Conference (WCNC), San Francisco, CA, 2017, pp. 1-6.
[2]. D. Lin, G. Charbit and I. K. Fu, "Uplink Contention Based Multiple Access for 5G
Cellular IoT," 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall),
Boston, MA, 2015, pp. 1-5.
[3]. S. Zhang, X. Xu, L. Lu, Y. Wu, G. He and Y. Chen, Sparse code multiple access: An
energy efficient uplink approach for 5G wireless systems," 2014 IEEE Global
Communications Conference, Austin, TX, 2014, pp. 4782-4787.
[4]. J. Wang, C. Zhang, R. Li, G. Wang and J. Wang, Narrow-Band SCMA: A New
Solution for 5G IoT Uplink Communications," 2016 IEEE 84th Vehicular Technology
Conference (VTC-Fall), Montreal, QC, 2016, pp. 1-5.

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[5]. K. Au et al., "Uplink contention based SCMA for 5G radio access," 2014 IEEE
Globecom Workshops (GCWkshps), Austin, TX, 2014, pp. 900-905.
[6]. J. Shen, W. Chen, F. Wei and Y. Wu, "ACK feedback based UE-to-CTU
mapping rule for SCMA uplink grant-free transmission," 2017 9th
International Conference on Wireless Communications and Signal
Processing (WCSP), Nanjing, 2017, pp. 1-6.Inf. Theory, vol. 59, no. 10, pp.
6859-6872, Oct. 2013.
[7] T. Park and W. Saad, "Learning with finite memory for machine type
communication," 2016 Annual Conference on Information Science and Systems
(CISS), Princeton, NJ, 2016, pp. 608-613.
[8] T. Park and W. Saad, "Resource Allocation and Coordination for Critical
Messages Using Finite Memory Learning," 2016 IEEE Globecom Workshops (GC
Wkshps), Washington, DC, 2016, pp. 1-6.
REFERENCES

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RESEARCH CONTRIBUTION
Bayisa Taye Mulatu, Zhenni Pan, Jiang Liu, Shigeru Shimamoto,
“Contention-Based SCMA for Narrow-Band Internet of Things using
Sequential Learning”, in a proceeding of the 37th JSST Annual
International Conference on Simulation Technology, Muroran
Institute of Technology, Muroran City, Hokkaido, Japan, September
18-20, 2018.

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